SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1001–1025 of 10307 papers

TitleStatusHype
Shared Data and Algorithms for Deep Learning in Fundamental PhysicsCode1
Learning Bounds for Open-Set LearningCode1
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing ModalitiesCode1
Time-Series Representation Learning via Temporal and Contextual ContrastingCode1
Teacher Model Fingerprinting Attacks Against Transfer LearningCode1
Neural Fashion Image Captioning : Accounting for Data DiversityCode1
GAIA: A Transfer Learning System of Object Detection that Fits Your NeedsCode1
Amalgamating Knowledge From Heterogeneous Graph Neural NetworksCode1
Golos: Russian Dataset for Speech ResearchCode1
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event ExtractionCode1
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
MoVi: A large multi-purpose human motion and video datasetCode1
Deep Subdomain Adaptation Network for Image ClassificationCode1
An Evaluation of Self-Supervised Pre-Training for Skin-Lesion AnalysisCode1
Positional Contrastive Learning for Volumetric Medical Image SegmentationCode1
Learning Stable Classifiers by Transferring Unstable FeaturesCode1
rSoccer: A Framework for Studying Reinforcement Learning in Small and Very Small Size Robot SoccerCode1
Zero-shot Node Classification with Decomposed Graph Prototype NetworkCode1
Improving weakly supervised sound event detection with self-supervised auxiliary tasksCode1
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse DetectionCode1
AUGNLG: Few-shot Natural Language Generation using Self-trained Data AugmentationCode1
Fair Normalizing FlowsCode1
Variational Information Bottleneck for Effective Low-Resource Fine-TuningCode1
Distilling Image Classifiers in Object DetectorsCode1
Show:102550
← PrevPage 41 of 413Next →
All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified